# Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder

arXiv: https://arxiv.org/abs/2301.05017 · HTML: https://mlsanity.com/paper/2301.05017/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [How Does Batch Normalization Help Optimization?](https://mlsanity.com/paper/1805.11604/) (2018)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018)
- [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019)
- [6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/1909.11315/) (2019)
- [RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection](https://mlsanity.com/paper/2007.00140/) (2020)
- [Waveform Learning for Next-Generation Wireless Communication Systems](https://mlsanity.com/paper/2109.00998/) (2021)
- [Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges](https://mlsanity.com/paper/2001.08159/) (2020)
- [Deep Learning Based Sphere Decoding](https://mlsanity.com/paper/1807.03162/) (2018)
- [Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication](https://mlsanity.com/paper/2203.07703/) (2022)
- [Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifier](https://mlsanity.com/paper/2201.05524/) (2022)
- [Learning OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2110.10987/) (2021)
- [Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder](https://mlsanity.com/paper/2011.06349/) (2020)

## Built on by (most cited first, 2 of 2)

- [Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems](https://mlsanity.com/paper/2510.25416/) (2025)
- [A Study of Neural Polar Decoders for Communication](https://mlsanity.com/paper/2510.03069/) (2025)

## Abstract (arXiv)

An enhanced framework for peak-to-average power ratio (mathsfPAPR) reduction and waveform design for Multiple-Input-Multiple-Output (mathsfMIMO) orthogonal frequency-division multiplexing (mathsfOFDM) systems, based on a convolutional-autoencoder (mathsfCAE) architecture, is presented. The end-to-end learning-based autoencoder (mathsfAE) for communication networks represents the network by an encoder and decoder, where in between, the learned latent representation goes through a physical communication channel. We introduce a joint learning scheme based on projected gradient descent iteration to optimize the spectral mask behavior and MIMO detection under the influence of a non-linear high power amplifier (mathsfHPA) and a multipath fading channel. The offered efficient implementation novel waveform design technique utilizes only a single mathsfPAPR reduction block for all antennas. It is throughput-lossless, as no side information is required at the decoder. Performance is analyzed by examining the bit error rate (mathsfBER), the mathsfPAPR, and the spectral response and compared with classical mathsfPAPR reduction mathsfMIMO detector methods on 5G simulated data. The suggested system exhibits competitive performance when considering all optimization criteria simultaneously. We apply gradual loss learning for multi-objective optimization and show empirically that a single trained model covers the tasks of mathsfPAPR reduction, spectrum design, and mathsfMIMO detection together over a wide range of SNR levels.